Decision making within intelligent systems, such as, for example, autonomous vehicles, typically relies on data provided by a variety of sensors. Evaluating a sensor's performance and the validity of its data are important to developing a system that is capable of detecting and responding to sensor failure, and capable of functioning within dynamic environmental conditions. Sensor data can become unreliable for a variety of reasons, including operating environment, age, occlusion, or physical misalignment. Current autonomous or unmanned vehicle systems generally rely on the ability to constrain environmental and operating conditions, and make the naïve assumption that all sensor data is accurate when delivered in the expected format. Redundant sensors may be added to perform data verification through cross-correlation; however, this can significantly increase the cost and complexity of the vehicle system without necessarily resolving the issue of data integrity. Some systems perform safety checks for the sensors that include periodic monitoring of a “heartbeat” signal and/or checking of the sensor data against a “token” (e.g., a relatively easily identified physical feature at a known location). These may provide some level of data validation, but only against the more obvious cases of sensor failure, for example, when the sensor stops sending data entirely. Furthermore, these techniques only provide a pass/fail (binary) signal, as opposed to a more useful confidence value, or probability, of data integrity.
In less obvious sensor failure conditions, which would not be detected by token detection or sensor heartbeats, incomplete or inaccurate sensor data may be sent to the vehicle's decision-making systems for further processing and evaluation without regard to its integrity or validity. This may lead to relatively serious system failures that can result, for example, in spurious braking and steering events or failure to identify obstacle information, which in turn can present serious safety risks in an autonomous vehicle system.